Overview

Dataset statistics

Number of variables21
Number of observations4119
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory675.9 KiB
Average record size in memory168.0 B

Variable types

Numeric10
Categorical10
Boolean1

Alerts

pdays is highly correlated with previous and 3 other fieldsHigh correlation
previous is highly correlated with pdays and 3 other fieldsHigh correlation
emp.var.rate is highly correlated with contact and 6 other fieldsHigh correlation
cons.price.idx is highly correlated with contact and 6 other fieldsHigh correlation
euribor3m is highly correlated with month and 6 other fieldsHigh correlation
nr.employed is highly correlated with contact and 9 other fieldsHigh correlation
loan is highly correlated with housingHigh correlation
month is highly correlated with contact and 5 other fieldsHigh correlation
housing is highly correlated with loanHigh correlation
contact is highly correlated with month and 4 other fieldsHigh correlation
job is highly correlated with educationHigh correlation
education is highly correlated with jobHigh correlation
duration is highly correlated with yHigh correlation
poutcome is highly correlated with pdays and 6 other fieldsHigh correlation
cons.conf.idx is highly correlated with contact and 8 other fieldsHigh correlation
y is highly correlated with duration and 2 other fieldsHigh correlation
previous has 3523 (85.5%) zeros Zeros

Reproduction

Analysis started2022-10-12 20:20:04.911108
Analysis finished2022-10-12 20:20:23.732493
Duration18.82 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

Distinct67
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.11361981
Minimum18
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2022-10-12T22:20:23.823174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum88
Range70
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.31336155
Coefficient of variation (CV)0.2571037367
Kurtosis0.4381297604
Mean40.11361981
Median Absolute Deviation (MAD)7
Skewness0.7156939791
Sum165228
Variance106.3654264
MonotonicityNot monotonic
2022-10-12T22:20:23.948929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32216
 
5.2%
31191
 
4.6%
30177
 
4.3%
34174
 
4.2%
35172
 
4.2%
33170
 
4.1%
36168
 
4.1%
38150
 
3.6%
41147
 
3.6%
29139
 
3.4%
Other values (57)2415
58.6%
ValueCountFrequency (%)
183
 
0.1%
191
 
< 0.1%
204
 
0.1%
217
 
0.2%
2210
 
0.2%
2316
 
0.4%
2457
1.4%
2557
1.4%
2662
1.5%
2787
2.1%
ValueCountFrequency (%)
881
 
< 0.1%
862
< 0.1%
851
 
< 0.1%
822
< 0.1%
813
0.1%
804
0.1%
783
0.1%
772
< 0.1%
764
0.1%
752
< 0.1%

job
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
admin.
1012 
blue-collar
884 
technician
691 
services
393 
management
324 
Other values (7)
815 

Length

Max length13
Median length12
Mean length8.992959456
Min length6

Characters and Unicode

Total characters37042
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblue-collar
2nd rowservices
3rd rowservices
4th rowservices
5th rowadmin.

Common Values

ValueCountFrequency (%)
admin.1012
24.6%
blue-collar884
21.5%
technician691
16.8%
services393
 
9.5%
management324
 
7.9%
retired166
 
4.0%
self-employed159
 
3.9%
entrepreneur148
 
3.6%
unemployed111
 
2.7%
housemaid110
 
2.7%
Other values (2)121
 
2.9%

Length

2022-10-12T22:20:24.068059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin1012
24.6%
blue-collar884
21.5%
technician691
16.8%
services393
 
9.5%
management324
 
7.9%
retired166
 
4.0%
self-employed159
 
3.9%
entrepreneur148
 
3.6%
unemployed111
 
2.7%
housemaid110
 
2.7%
Other values (2)121
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e4824
13.0%
n3648
 
9.8%
a3345
 
9.0%
l3081
 
8.3%
i3063
 
8.3%
c2659
 
7.2%
r2053
 
5.5%
m2040
 
5.5%
d1640
 
4.4%
t1493
 
4.0%
Other values (14)9196
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34987
94.5%
Dash Punctuation1043
 
2.8%
Other Punctuation1012
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4824
13.8%
n3648
10.4%
a3345
9.6%
l3081
8.8%
i3063
8.8%
c2659
 
7.6%
r2053
 
5.9%
m2040
 
5.8%
d1640
 
4.7%
t1493
 
4.3%
Other values (12)7141
20.4%
Dash Punctuation
ValueCountFrequency (%)
-1043
100.0%
Other Punctuation
ValueCountFrequency (%)
.1012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin34987
94.5%
Common2055
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4824
13.8%
n3648
10.4%
a3345
9.6%
l3081
8.8%
i3063
8.8%
c2659
 
7.6%
r2053
 
5.9%
m2040
 
5.8%
d1640
 
4.7%
t1493
 
4.3%
Other values (12)7141
20.4%
Common
ValueCountFrequency (%)
-1043
50.8%
.1012
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII37042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4824
13.0%
n3648
 
9.8%
a3345
 
9.0%
l3081
 
8.3%
i3063
 
8.3%
c2659
 
7.2%
r2053
 
5.5%
m2040
 
5.5%
d1640
 
4.4%
t1493
 
4.0%
Other values (14)9196
24.8%

marital
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
married
2509 
single
1153 
divorced
446 
unknown
 
11

Length

Max length8
Median length7
Mean length6.828356397
Min length6

Characters and Unicode

Total characters28126
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowsingle
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married2509
60.9%
single1153
28.0%
divorced446
 
10.8%
unknown11
 
0.3%

Length

2022-10-12T22:20:24.169259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:24.285194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
married2509
60.9%
single1153
28.0%
divorced446
 
10.8%
unknown11
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r5464
19.4%
i4108
14.6%
e4108
14.6%
d3401
12.1%
m2509
8.9%
a2509
8.9%
n1186
 
4.2%
s1153
 
4.1%
g1153
 
4.1%
l1153
 
4.1%
Other values (6)1382
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28126
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r5464
19.4%
i4108
14.6%
e4108
14.6%
d3401
12.1%
m2509
8.9%
a2509
8.9%
n1186
 
4.2%
s1153
 
4.1%
g1153
 
4.1%
l1153
 
4.1%
Other values (6)1382
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin28126
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r5464
19.4%
i4108
14.6%
e4108
14.6%
d3401
12.1%
m2509
8.9%
a2509
8.9%
n1186
 
4.2%
s1153
 
4.1%
g1153
 
4.1%
l1153
 
4.1%
Other values (6)1382
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII28126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r5464
19.4%
i4108
14.6%
e4108
14.6%
d3401
12.1%
m2509
8.9%
a2509
8.9%
n1186
 
4.2%
s1153
 
4.1%
g1153
 
4.1%
l1153
 
4.1%
Other values (6)1382
 
4.9%

education
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
university.degree
1264 
high.school
921 
basic.9y
574 
professional.course
535 
basic.4y
429 
Other values (3)
396 

Length

Max length19
Median length17
Mean length12.82131585
Min length7

Characters and Unicode

Total characters52811
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowbasic.9y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.9y
5th rowuniversity.degree

Common Values

ValueCountFrequency (%)
university.degree1264
30.7%
high.school921
22.4%
basic.9y574
13.9%
professional.course535
13.0%
basic.4y429
 
10.4%
basic.6y228
 
5.5%
unknown167
 
4.1%
illiterate1
 
< 0.1%

Length

2022-10-12T22:20:24.383400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:24.499321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
university.degree1264
30.7%
high.school921
22.4%
basic.9y574
13.9%
professional.course535
13.0%
basic.4y429
 
10.4%
basic.6y228
 
5.5%
unknown167
 
4.1%
illiterate1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e6128
11.6%
i5217
 
9.9%
s5021
 
9.5%
.3951
 
7.5%
o3614
 
6.8%
r3599
 
6.8%
h2763
 
5.2%
c2687
 
5.1%
y2495
 
4.7%
n2300
 
4.4%
Other values (15)15036
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47629
90.2%
Other Punctuation3951
 
7.5%
Decimal Number1231
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6128
12.9%
i5217
11.0%
s5021
10.5%
o3614
 
7.6%
r3599
 
7.6%
h2763
 
5.8%
c2687
 
5.6%
y2495
 
5.2%
n2300
 
4.8%
g2185
 
4.6%
Other values (11)11620
24.4%
Decimal Number
ValueCountFrequency (%)
9574
46.6%
4429
34.8%
6228
 
18.5%
Other Punctuation
ValueCountFrequency (%)
.3951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47629
90.2%
Common5182
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6128
12.9%
i5217
11.0%
s5021
10.5%
o3614
 
7.6%
r3599
 
7.6%
h2763
 
5.8%
c2687
 
5.6%
y2495
 
5.2%
n2300
 
4.8%
g2185
 
4.6%
Other values (11)11620
24.4%
Common
ValueCountFrequency (%)
.3951
76.2%
9574
 
11.1%
4429
 
8.3%
6228
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII52811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6128
11.6%
i5217
 
9.9%
s5021
 
9.5%
.3951
 
7.5%
o3614
 
6.8%
r3599
 
6.8%
h2763
 
5.2%
c2687
 
5.1%
y2495
 
4.7%
n2300
 
4.4%
Other values (15)15036
28.5%

default
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
no
3315 
unknown
803 
yes
 
1

Length

Max length7
Median length2
Mean length2.974993931
Min length2

Characters and Unicode

Total characters12254
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no3315
80.5%
unknown803
 
19.5%
yes1
 
< 0.1%

Length

2022-10-12T22:20:24.615350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:24.712690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no3315
80.5%
unknown803
 
19.5%
yes1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n5724
46.7%
o4118
33.6%
u803
 
6.6%
k803
 
6.6%
w803
 
6.6%
y1
 
< 0.1%
e1
 
< 0.1%
s1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12254
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n5724
46.7%
o4118
33.6%
u803
 
6.6%
k803
 
6.6%
w803
 
6.6%
y1
 
< 0.1%
e1
 
< 0.1%
s1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin12254
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n5724
46.7%
o4118
33.6%
u803
 
6.6%
k803
 
6.6%
w803
 
6.6%
y1
 
< 0.1%
e1
 
< 0.1%
s1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n5724
46.7%
o4118
33.6%
u803
 
6.6%
k803
 
6.6%
w803
 
6.6%
y1
 
< 0.1%
e1
 
< 0.1%
s1
 
< 0.1%

housing
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
yes
2175 
no
1839 
unknown
 
105

Length

Max length7
Median length3
Mean length2.655498908
Min length2

Characters and Unicode

Total characters10938
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowyes
4th rowunknown
5th rowyes

Common Values

ValueCountFrequency (%)
yes2175
52.8%
no1839
44.6%
unknown105
 
2.5%

Length

2022-10-12T22:20:24.801414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:24.902848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
yes2175
52.8%
no1839
44.6%
unknown105
 
2.5%

Most occurring characters

ValueCountFrequency (%)
y2175
19.9%
e2175
19.9%
s2175
19.9%
n2154
19.7%
o1944
17.8%
u105
 
1.0%
k105
 
1.0%
w105
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10938
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y2175
19.9%
e2175
19.9%
s2175
19.9%
n2154
19.7%
o1944
17.8%
u105
 
1.0%
k105
 
1.0%
w105
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10938
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
y2175
19.9%
e2175
19.9%
s2175
19.9%
n2154
19.7%
o1944
17.8%
u105
 
1.0%
k105
 
1.0%
w105
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y2175
19.9%
e2175
19.9%
s2175
19.9%
n2154
19.7%
o1944
17.8%
u105
 
1.0%
k105
 
1.0%
w105
 
1.0%

loan
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
no
3349 
yes
665 
unknown
 
105

Length

Max length7
Median length2
Mean length2.288905074
Min length2

Characters and Unicode

Total characters9428
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowunknown
5th rowno

Common Values

ValueCountFrequency (%)
no3349
81.3%
yes665
 
16.1%
unknown105
 
2.5%

Length

2022-10-12T22:20:24.992128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:25.091448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no3349
81.3%
yes665
 
16.1%
unknown105
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n3664
38.9%
o3454
36.6%
y665
 
7.1%
e665
 
7.1%
s665
 
7.1%
u105
 
1.1%
k105
 
1.1%
w105
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9428
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n3664
38.9%
o3454
36.6%
y665
 
7.1%
e665
 
7.1%
s665
 
7.1%
u105
 
1.1%
k105
 
1.1%
w105
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin9428
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n3664
38.9%
o3454
36.6%
y665
 
7.1%
e665
 
7.1%
s665
 
7.1%
u105
 
1.1%
k105
 
1.1%
w105
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n3664
38.9%
o3454
36.6%
y665
 
7.1%
e665
 
7.1%
s665
 
7.1%
u105
 
1.1%
k105
 
1.1%
w105
 
1.1%

contact
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
cellular
2652 
telephone
1467 

Length

Max length9
Median length8
Mean length8.356154406
Min length8

Characters and Unicode

Total characters34419
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular2652
64.4%
telephone1467
35.6%

Length

2022-10-12T22:20:25.177995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:25.273579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
cellular2652
64.4%
telephone1467
35.6%

Most occurring characters

ValueCountFrequency (%)
l9423
27.4%
e7053
20.5%
c2652
 
7.7%
u2652
 
7.7%
a2652
 
7.7%
r2652
 
7.7%
t1467
 
4.3%
p1467
 
4.3%
h1467
 
4.3%
o1467
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34419
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l9423
27.4%
e7053
20.5%
c2652
 
7.7%
u2652
 
7.7%
a2652
 
7.7%
r2652
 
7.7%
t1467
 
4.3%
p1467
 
4.3%
h1467
 
4.3%
o1467
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin34419
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l9423
27.4%
e7053
20.5%
c2652
 
7.7%
u2652
 
7.7%
a2652
 
7.7%
r2652
 
7.7%
t1467
 
4.3%
p1467
 
4.3%
h1467
 
4.3%
o1467
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII34419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l9423
27.4%
e7053
20.5%
c2652
 
7.7%
u2652
 
7.7%
a2652
 
7.7%
r2652
 
7.7%
t1467
 
4.3%
p1467
 
4.3%
h1467
 
4.3%
o1467
 
4.3%

month
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
may
1378 
jul
711 
aug
636 
jun
530 
nov
446 
Other values (5)
418 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12357
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowjun
4th rowjun
5th rownov

Common Values

ValueCountFrequency (%)
may1378
33.5%
jul711
17.3%
aug636
15.4%
jun530
 
12.9%
nov446
 
10.8%
apr215
 
5.2%
oct69
 
1.7%
sep64
 
1.6%
mar48
 
1.2%
dec22
 
0.5%

Length

2022-10-12T22:20:25.358621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:25.638368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
may1378
33.5%
jul711
17.3%
aug636
15.4%
jun530
 
12.9%
nov446
 
10.8%
apr215
 
5.2%
oct69
 
1.7%
sep64
 
1.6%
mar48
 
1.2%
dec22
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a2277
18.4%
u1877
15.2%
m1426
11.5%
y1378
11.2%
j1241
10.0%
n976
7.9%
l711
 
5.8%
g636
 
5.1%
o515
 
4.2%
v446
 
3.6%
Other values (7)874
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12357
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2277
18.4%
u1877
15.2%
m1426
11.5%
y1378
11.2%
j1241
10.0%
n976
7.9%
l711
 
5.8%
g636
 
5.1%
o515
 
4.2%
v446
 
3.6%
Other values (7)874
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin12357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2277
18.4%
u1877
15.2%
m1426
11.5%
y1378
11.2%
j1241
10.0%
n976
7.9%
l711
 
5.8%
g636
 
5.1%
o515
 
4.2%
v446
 
3.6%
Other values (7)874
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2277
18.4%
u1877
15.2%
m1426
11.5%
y1378
11.2%
j1241
10.0%
n976
7.9%
l711
 
5.8%
g636
 
5.1%
o515
 
4.2%
v446
 
3.6%
Other values (7)874
 
7.1%

day_of_week
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
thu
860 
mon
855 
tue
841 
wed
795 
fri
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12357
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfri
2nd rowfri
3rd rowwed
4th rowfri
5th rowmon

Common Values

ValueCountFrequency (%)
thu860
20.9%
mon855
20.8%
tue841
20.4%
wed795
19.3%
fri768
18.6%

Length

2022-10-12T22:20:25.754496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:25.859747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
thu860
20.9%
mon855
20.8%
tue841
20.4%
wed795
19.3%
fri768
18.6%

Most occurring characters

ValueCountFrequency (%)
t1701
13.8%
u1701
13.8%
e1636
13.2%
h860
7.0%
m855
6.9%
o855
6.9%
n855
6.9%
w795
6.4%
d795
6.4%
f768
6.2%
Other values (2)1536
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12357
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1701
13.8%
u1701
13.8%
e1636
13.2%
h860
7.0%
m855
6.9%
o855
6.9%
n855
6.9%
w795
6.4%
d795
6.4%
f768
6.2%
Other values (2)1536
12.4%

Most occurring scripts

ValueCountFrequency (%)
Latin12357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1701
13.8%
u1701
13.8%
e1636
13.2%
h860
7.0%
m855
6.9%
o855
6.9%
n855
6.9%
w795
6.4%
d795
6.4%
f768
6.2%
Other values (2)1536
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII12357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1701
13.8%
u1701
13.8%
e1636
13.2%
h860
7.0%
m855
6.9%
o855
6.9%
n855
6.9%
w795
6.4%
d795
6.4%
f768
6.2%
Other values (2)1536
12.4%

duration
Real number (ℝ≥0)

HIGH CORRELATION

Distinct828
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.7880554
Minimum0
Maximum3643
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2022-10-12T22:20:25.975122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1103
median181
Q3317
95-th percentile740.2
Maximum3643
Range3643
Interquartile range (IQR)214

Descriptive statistics

Standard deviation254.7037361
Coefficient of variation (CV)0.9918831145
Kurtosis20.76192927
Mean256.7880554
Median Absolute Deviation (MAD)92
Skewness3.294781323
Sum1057710
Variance64873.99319
MonotonicityNot monotonic
2022-10-12T22:20:26.090539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7724
 
0.6%
11223
 
0.6%
7322
 
0.5%
8121
 
0.5%
12220
 
0.5%
11320
 
0.5%
9020
 
0.5%
14520
 
0.5%
8320
 
0.5%
11419
 
0.5%
Other values (818)3910
94.9%
ValueCountFrequency (%)
01
 
< 0.1%
41
 
< 0.1%
54
 
0.1%
65
0.1%
74
 
0.1%
86
0.1%
99
0.2%
1010
0.2%
118
0.2%
126
0.1%
ValueCountFrequency (%)
36431
< 0.1%
32531
< 0.1%
26531
< 0.1%
23011
< 0.1%
19801
< 0.1%
18681
< 0.1%
18552
< 0.1%
18201
< 0.1%
18061
< 0.1%
17201
< 0.1%

campaign
Real number (ℝ≥0)

Distinct25
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.537266327
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2022-10-12T22:20:26.200700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum35
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.568159238
Coefficient of variation (CV)1.012175667
Kurtosis25.28452046
Mean2.537266327
Median Absolute Deviation (MAD)1
Skewness4.003184952
Sum10451
Variance6.59544187
MonotonicityNot monotonic
2022-10-12T22:20:26.293735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11764
42.8%
21039
25.2%
3549
 
13.3%
4291
 
7.1%
5142
 
3.4%
699
 
2.4%
760
 
1.5%
836
 
0.9%
932
 
0.8%
1020
 
0.5%
Other values (15)87
 
2.1%
ValueCountFrequency (%)
11764
42.8%
21039
25.2%
3549
 
13.3%
4291
 
7.1%
5142
 
3.4%
699
 
2.4%
760
 
1.5%
836
 
0.9%
932
 
0.8%
1020
 
0.5%
ValueCountFrequency (%)
351
 
< 0.1%
292
 
< 0.1%
271
 
< 0.1%
241
 
< 0.1%
232
 
< 0.1%
222
 
< 0.1%
192
 
< 0.1%
181
 
< 0.1%
1714
0.3%
167
0.2%

pdays
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean960.4221899
Minimum0
Maximum999
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2022-10-12T22:20:26.392159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation191.9227858
Coefficient of variation (CV)0.1998316863
Kurtosis20.81248388
Mean960.4221899
Median Absolute Deviation (MAD)0
Skewness-4.775139161
Sum3955979
Variance36834.35571
MonotonicityNot monotonic
2022-10-12T22:20:26.485537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
9993959
96.1%
352
 
1.3%
642
 
1.0%
414
 
0.3%
710
 
0.2%
108
 
0.2%
125
 
0.1%
54
 
0.1%
24
 
0.1%
13
 
0.1%
Other values (11)18
 
0.4%
ValueCountFrequency (%)
02
 
< 0.1%
13
 
0.1%
24
 
0.1%
352
1.3%
414
 
0.3%
54
 
0.1%
642
1.0%
710
 
0.2%
93
 
0.1%
108
 
0.2%
ValueCountFrequency (%)
9993959
96.1%
211
 
< 0.1%
191
 
< 0.1%
182
 
< 0.1%
171
 
< 0.1%
162
 
< 0.1%
152
 
< 0.1%
141
 
< 0.1%
132
 
< 0.1%
125
 
0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1903374605
Minimum0
Maximum6
Zeros3523
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2022-10-12T22:20:26.573143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5417883234
Coefficient of variation (CV)2.846461868
Kurtosis22.12032347
Mean0.1903374605
Median Absolute Deviation (MAD)0
Skewness4.022978833
Sum784
Variance0.2935345874
MonotonicityNot monotonic
2022-10-12T22:20:26.648630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
03523
85.5%
1475
 
11.5%
278
 
1.9%
325
 
0.6%
414
 
0.3%
52
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
03523
85.5%
1475
 
11.5%
278
 
1.9%
325
 
0.6%
414
 
0.3%
52
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
52
 
< 0.1%
414
 
0.3%
325
 
0.6%
278
 
1.9%
1475
 
11.5%
03523
85.5%

poutcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
nonexistent
3523 
failure
454 
success
 
142

Length

Max length11
Median length11
Mean length10.42121874
Min length7

Characters and Unicode

Total characters42925
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent3523
85.5%
failure454
 
11.0%
success142
 
3.4%

Length

2022-10-12T22:20:26.745783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T22:20:26.850846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent3523
85.5%
failure454
 
11.0%
success142
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n10569
24.6%
e7642
17.8%
t7046
16.4%
i3977
 
9.3%
s3949
 
9.2%
o3523
 
8.2%
x3523
 
8.2%
u596
 
1.4%
f454
 
1.1%
a454
 
1.1%
Other values (3)1192
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter42925
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n10569
24.6%
e7642
17.8%
t7046
16.4%
i3977
 
9.3%
s3949
 
9.2%
o3523
 
8.2%
x3523
 
8.2%
u596
 
1.4%
f454
 
1.1%
a454
 
1.1%
Other values (3)1192
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin42925
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n10569
24.6%
e7642
17.8%
t7046
16.4%
i3977
 
9.3%
s3949
 
9.2%
o3523
 
8.2%
x3523
 
8.2%
u596
 
1.4%
f454
 
1.1%
a454
 
1.1%
Other values (3)1192
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII42925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n10569
24.6%
e7642
17.8%
t7046
16.4%
i3977
 
9.3%
s3949
 
9.2%
o3523
 
8.2%
x3523
 
8.2%
u596
 
1.4%
f454
 
1.1%
a454
 
1.1%
Other values (3)1192
 
2.8%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0849720806
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative1735
Negative (%)42.1%
Memory size32.3 KiB
2022-10-12T22:20:26.926878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.563114456
Coefficient of variation (CV)18.39562413
Kurtosis-1.041783886
Mean0.0849720806
Median Absolute Deviation (MAD)0.3
Skewness-0.7276878782
Sum350
Variance2.443326802
MonotonicityNot monotonic
2022-10-12T22:20:27.006359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.41626
39.5%
-1.8883
21.4%
1.1758
18.4%
-0.1392
 
9.5%
-2.9164
 
4.0%
-3.4104
 
2.5%
-1.787
 
2.1%
-1.183
 
2.0%
-321
 
0.5%
-0.21
 
< 0.1%
ValueCountFrequency (%)
-3.4104
 
2.5%
-321
 
0.5%
-2.9164
 
4.0%
-1.8883
21.4%
-1.787
 
2.1%
-1.183
 
2.0%
-0.21
 
< 0.1%
-0.1392
 
9.5%
1.1758
18.4%
1.41626
39.5%
ValueCountFrequency (%)
1.41626
39.5%
1.1758
18.4%
-0.1392
 
9.5%
-0.21
 
< 0.1%
-1.183
 
2.0%
-1.787
 
2.1%
-1.8883
21.4%
-2.9164
 
4.0%
-321
 
0.5%
-3.4104
 
2.5%

cons.price.idx
Real number (ℝ≥0)

HIGH CORRELATION

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.5797043
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2022-10-12T22:20:27.091914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.579348805
Coefficient of variation (CV)0.0061909664
Kurtosis-0.8233578937
Mean93.5797043
Median Absolute Deviation (MAD)0.38
Skewness-0.2166414217
Sum385454.802
Variance0.3356450378
MonotonicityNot monotonic
2022-10-12T22:20:27.192767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994758
18.4%
93.918667
16.2%
92.893597
14.5%
93.444528
12.8%
94.465431
10.5%
93.2386
9.4%
93.075201
 
4.9%
92.96375
 
1.8%
92.20175
 
1.8%
92.43143
 
1.0%
Other values (16)358
8.7%
ValueCountFrequency (%)
92.20175
 
1.8%
92.37925
 
0.6%
92.43143
 
1.0%
92.46914
 
0.3%
92.64936
 
0.9%
92.71321
 
0.5%
92.7561
 
< 0.1%
92.84325
 
0.6%
92.893597
14.5%
92.96375
 
1.8%
ValueCountFrequency (%)
94.76724
 
0.6%
94.60120
 
0.5%
94.465431
10.5%
94.21530
 
0.7%
94.19939
 
0.9%
94.05524
 
0.6%
94.02733
 
0.8%
93.994758
18.4%
93.918667
16.2%
93.87623
 
0.6%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.49910172
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative4119
Negative (%)100.0%
Memory size32.3 KiB
2022-10-12T22:20:27.290461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.594577507
Coefficient of variation (CV)-0.1134488745
Kurtosis-0.3143030044
Mean-40.49910172
Median Absolute Deviation (MAD)4.4
Skewness0.2873090796
Sum-166815.8
Variance21.11014247
MonotonicityNot monotonic
2022-10-12T22:20:27.385266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4758
18.4%
-42.7667
16.2%
-46.2597
14.5%
-36.1528
12.8%
-41.8431
10.5%
-42386
9.4%
-47.1201
 
4.9%
-40.875
 
1.8%
-31.475
 
1.8%
-26.943
 
1.0%
Other values (16)358
8.7%
ValueCountFrequency (%)
-50.824
 
0.6%
-5025
 
0.6%
-49.520
 
0.5%
-47.1201
 
4.9%
-46.2597
14.5%
-45.91
 
< 0.1%
-42.7667
16.2%
-42386
9.4%
-41.8431
10.5%
-40.875
 
1.8%
ValueCountFrequency (%)
-26.943
 
1.0%
-29.825
 
0.6%
-30.136
 
0.9%
-31.475
 
1.8%
-3321
 
0.5%
-33.614
 
0.3%
-34.614
 
0.3%
-34.823
 
0.6%
-36.1528
12.8%
-36.4758
18.4%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION

Distinct234
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621355669
Minimum0.635
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2022-10-12T22:20:27.496916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.635
5-th percentile0.8084
Q11.334
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.41
Interquartile range (IQR)3.627

Descriptive statistics

Standard deviation1.733591223
Coefficient of variation (CV)0.4787133276
Kurtosis-1.396366286
Mean3.621355669
Median Absolute Deviation (MAD)0.108
Skewness-0.7150798684
Sum14916.364
Variance3.005338527
MonotonicityNot monotonic
2022-10-12T22:20:27.611422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857274
 
6.7%
4.963256
 
6.2%
4.962237
 
5.8%
4.961212
 
5.1%
4.856138
 
3.4%
4.965114
 
2.8%
4.964110
 
2.7%
1.405106
 
2.6%
4.96105
 
2.5%
4.968101
 
2.5%
Other values (224)2466
59.9%
ValueCountFrequency (%)
0.6353
0.1%
0.6361
 
< 0.1%
0.6371
 
< 0.1%
0.6392
 
< 0.1%
0.641
 
< 0.1%
0.6421
 
< 0.1%
0.6432
 
< 0.1%
0.6445
0.1%
0.6452
 
< 0.1%
0.6464
0.1%
ValueCountFrequency (%)
5.0451
 
< 0.1%
4.9721
 
0.5%
4.968101
 
2.5%
4.96762
 
1.5%
4.96672
 
1.7%
4.965114
2.8%
4.964110
2.7%
4.963256
6.2%
4.962237
5.8%
4.961212
5.1%

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5166.481695
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.3 KiB
2022-10-12T22:20:27.707401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation73.66790356
Coefficient of variation (CV)0.01425881439
Kurtosis0.0617241978
Mean5166.481695
Median Absolute Deviation (MAD)37.1
Skewness-1.075876888
Sum21280738.1
Variance5426.960015
MonotonicityNot monotonic
2022-10-12T22:20:27.790497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.11626
39.5%
5099.1823
20.0%
5191758
18.4%
5195.8392
 
9.5%
5076.2164
 
4.0%
5017.5104
 
2.5%
4991.687
 
2.1%
4963.683
 
2.0%
5008.760
 
1.5%
5023.521
 
0.5%
ValueCountFrequency (%)
4963.683
 
2.0%
4991.687
 
2.1%
5008.760
 
1.5%
5017.5104
 
2.5%
5023.521
 
0.5%
5076.2164
 
4.0%
5099.1823
20.0%
5176.31
 
< 0.1%
5191758
18.4%
5195.8392
9.5%
ValueCountFrequency (%)
5228.11626
39.5%
5195.8392
 
9.5%
5191758
18.4%
5176.31
 
< 0.1%
5099.1823
20.0%
5076.2164
 
4.0%
5023.521
 
0.5%
5017.5104
 
2.5%
5008.760
 
1.5%
4991.687
 
2.1%

y
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
3668 
True
451 
ValueCountFrequency (%)
False3668
89.1%
True451
 
10.9%
2022-10-12T22:20:27.888590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2022-10-12T22:20:22.225404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:12.922785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.080343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.105343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.124281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.220549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.215616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.181663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.160131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.265352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.327531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.166509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.188128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.209809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.230041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.325565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.316706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.284432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.263201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.366090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.429177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.273963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.295664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.316191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.444351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.429653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.417492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.385838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.364840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.466789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.530094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.381470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.401986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.420821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.546882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.533769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.519187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.489174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.596285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.568229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.625304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.480665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.502647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.519408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.643923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.631228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.613594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.584512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.691620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.664067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.723751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.584772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.605878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.621640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.743461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.731953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.711962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.684777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.792397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.761831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.816639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.682466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.705131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.718300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.839236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.827509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.804950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.778806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.887601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.853551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.913028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.783438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.805046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.818517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.935187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.924746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.901182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.874820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.983451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.947568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:23.007956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.882938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:14.905686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.919626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.030596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.022489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.994408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.970453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.077289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.040114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:23.100832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:13.980392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:15.005227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:16.020486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:17.125037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:18.119333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:19.088500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:20.065802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:21.171394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-12T22:20:22.132794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-10-12T22:20:27.967511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-12T22:20:28.105113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-12T22:20:28.241831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-12T22:20:28.380768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-12T22:20:28.524872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-12T22:20:23.283112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-12T22:20:23.616405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
030blue-collarmarriedbasic.9ynoyesnocellularmayfri48729990nonexistent-1.892.893-46.21.3135099.1no
139servicessinglehigh.schoolnononotelephonemayfri34649990nonexistent1.193.994-36.44.8555191.0no
225servicesmarriedhigh.schoolnoyesnotelephonejunwed22719990nonexistent1.494.465-41.84.9625228.1no
338servicesmarriedbasic.9ynounknownunknowntelephonejunfri1739990nonexistent1.494.465-41.84.9595228.1no
447admin.marrieduniversity.degreenoyesnocellularnovmon5819990nonexistent-0.193.200-42.04.1915195.8no
532servicessingleuniversity.degreenononocellularsepthu12839992failure-1.194.199-37.50.8844963.6no
632admin.singleuniversity.degreenoyesnocellularsepmon29049990nonexistent-1.194.199-37.50.8794963.6no
741entrepreneurmarrieduniversity.degreeunknownyesnocellularnovmon4429990nonexistent-0.193.200-42.04.1915195.8no
831servicesdivorcedprofessional.coursenononocellularnovtue6819991failure-0.193.200-42.04.1535195.8no
935blue-collarmarriedbasic.9yunknownnonotelephonemaythu17019990nonexistent1.193.994-36.44.8555191.0no

Last rows

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
410963retiredmarriedhigh.schoolnononocellularoctwed138619990nonexistent-3.492.431-26.90.7405017.5no
411053housemaiddivorcedbasic.6yunknownunknownunknowntelephonemayfri8529990nonexistent1.193.994-36.44.8555191.0no
411130technicianmarrieduniversity.degreenonoyescellularjunfri13119991failure-1.794.055-39.80.7484991.6no
411231techniciansingleprofessional.coursenoyesnocellularnovthu15519990nonexistent-0.193.200-42.04.0765195.8no
411331admin.singleuniversity.degreenoyesnocellularnovthu46319990nonexistent-0.193.200-42.04.0765195.8no
411430admin.marriedbasic.6ynoyesyescellularjulthu5319990nonexistent1.493.918-42.74.9585228.1no
411539admin.marriedhigh.schoolnoyesnotelephonejulfri21919990nonexistent1.493.918-42.74.9595228.1no
411627studentsinglehigh.schoolnononocellularmaymon6429991failure-1.892.893-46.21.3545099.1no
411758admin.marriedhigh.schoolnononocellularaugfri52819990nonexistent1.493.444-36.14.9665228.1no
411834managementsinglehigh.schoolnoyesnocellularnovwed17519990nonexistent-0.193.200-42.04.1205195.8no